Sources of Validity Evidence Needed With Self-Report Measures of Physical Activity
Bibliographic record
Abstract
BACKGROUND: Over the years, self-report measures of physical activity (PA) have been employed in applications for which their use was not supported by the validity evidence. METHODS: To address this concern this paper 1) provided an overview of the sources of validity evidence that can be assessed with self-report measures of PA, 2) discussed the validity evidence needed to support the use of self-report in certain applications, and 3) conducted a case review of the 7-day PA Recall (7-d PAR). RESULTS: This paper discussed 5 sources of validity evidence, those based on: test content; response processes; behavioral stability; relations with other variables; and sensitivity to change. The evidence needed to use self-report measures of PA in epidemiological, surveillance, and intervention studies was presented. These concepts were applied to a case review of the 7-d PAR. The review highlighted the utility of the 7-d PAR to produce valid rankings. Initial support, albeit weaker, for using the 7-d PAR to detect relative change in PA behavior was found. CONCLUSION: Overall, self-report measures can validly rank PA behavior but they cannot adequately quantify PA. There is a need to improve the accuracy of self-report measures of PA to provide unbiased estimates of PA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.473 | 0.840 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.029 | 0.017 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".